Matching Items (890)
Filtering by
- All Subjects: Computer Science
- All Subjects: Materials Science
- All Subjects: Biomedical Engineering
- Genre: Doctoral Dissertation
- Resource Type: Text
Description
Fuel cells, particularly solid oxide fuel cells (SOFC), are important for the future of greener and more efficient energy sources. Although SOFCs have been in existence for over fifty years, they have not been deployed extensively because they need to be operated at a high temperature (∼1000 °C), are expensive, and have slow response to changes in energy demands. One important need for commercialization of SOFCs is a lowering of their operating temperature, which requires an electrolyte that can operate at lower temperatures. Doped ceria is one such candidate. For this dissertation work I have studied different types of doped ceria to understand the mechanism of oxygen vacancy diffusion through the bulk. Doped ceria is important because they have high ionic conductivities thus making them attractive candidates for the electrolytes of solid oxide fuel cells. In particular, I have studied how the ionic conductivities are improved in these doped materials by studying the oxygen-vacancy formations and migrations. In this dissertation I describe the application of density functional theory (DFT) and Kinetic Lattice Monte Carlo (KLMC) simulations to calculate the vacancy diffusion and ionic conductivities in doped ceria. The dopants used are praseodymium (Pr), gadolinium (Gd), and neodymium (Nd), all belonging to the lanthanide series. The activation energies for vacancy migration between different nearest neighbor (relative to the dopant) positions were calculated using the commercial DFT code VASP (Vienna Ab-initio Simulation Package). These activation energies were then used as inputs to the KLMC code that I co-developed. The KLMC code was run for different temperatures (673 K to 1073 K) and for different dopant concentrations (0 to 40%). These simulations have resulted in the prediction of dopant concentrations for maximum ionic conductivity at a given temperature.
ContributorsAnwar, Shahriar (Author) / Adams, James B (Thesis advisor) / Crozier, Peter (Committee member) / Krause, Stephen (Committee member) / Arizona State University (Publisher)
Created2011
Description
With the increasing focus on developing environmentally benign electronic packages, lead-free solder alloys have received a great deal of attention. Mishandling of packages, during manufacture, assembly, or by the user may cause failure of solder joint. A fundamental understanding of the behavior of lead-free solders under mechanical shock conditions is lacking. Reliable experimental and numerical analysis of lead-free solder joints in the intermediate strain rate regime need to be investigated. This dissertation mainly focuses on exploring the mechanical shock behavior of lead-free tin-rich solder alloys via multiscale modeling and numerical simulations. First, the macroscopic stress/strain behaviors of three bulk lead-free tin-rich solders were tested over a range of strain rates from 0.001/s to 30/s. Finite element analysis was conducted to determine appropriate specimen geometry that could reach a homogeneous stress/strain field and a relatively high strain rate. A novel self-consistent true stress correction method is developed to compensate the inaccuracy caused by the triaxial stress state at the post-necking stage. Then the material property of micron-scale intermetallic was examined by micro-compression test. The accuracy of this measure is systematically validated by finite element analysis, and empirical adjustments are provided. Moreover, the interfacial property of the solder/intermetallic interface is investigated, and a continuum traction-separation law of this interface is developed from an atomistic-based cohesive element method. The macroscopic stress/strain relation and microstructural properties are combined together to form a multiscale material behavior via a stochastic approach for both solder and intermetallic. As a result, solder is modeled by porous plasticity with random voids, and intermetallic is characterized as brittle material with random vulnerable region. Thereafter, the porous plasticity fracture of the solders and the brittle fracture of the intermetallics are coupled together in one finite element model. Finally, this study yields a multiscale model to understand and predict the mechanical shock behavior of lead-free tin-rich solder joints. Different fracture patterns are observed for various strain rates and/or intermetallic thicknesses. The predictions have a good agreement with the theory and experiments.
ContributorsFei, Huiyang (Author) / Jiang, Hanqing (Thesis advisor) / Chawla, Nikhilesh (Thesis advisor) / Tasooji, Amaneh (Committee member) / Mobasher, Barzin (Committee member) / Rajan, Subramaniam D. (Committee member) / Arizona State University (Publisher)
Created2011
Description
As pointed out in the keynote speech by H. V. Jagadish in SIGMOD'07, and also commonly agreed in the database community, the usability of structured data by casual users is as important as the data management systems' functionalities. A major hardness of using structured data is the problem of easily retrieving information from them given a user's information needs. Learning and using a structured query language (e.g., SQL and XQuery) is overwhelmingly burdensome for most users, as not only are these languages sophisticated, but the users need to know the data schema. Keyword search provides us with opportunities to conveniently access structured data and potentially significantly enhances the usability of structured data. However, processing keyword search on structured data is challenging due to various types of ambiguities such as structural ambiguity (keyword queries have no structure), keyword ambiguity (the keywords may not be accurate), user preference ambiguity (the user may have implicit preferences that are not indicated in the query), as well as the efficiency challenges due to large search space. This dissertation performs an expansive study on keyword search processing techniques as a gateway for users to access structured data and retrieve desired information. The key issues addressed include: (1) Resolving structural ambiguities in keyword queries by generating meaningful query results, which involves identifying relevant keyword matches, identifying return information, composing query results based on relevant matches and return information. (2) Resolving structural, keyword and user preference ambiguities through result analysis, including snippet generation, result differentiation, result clustering, result summarization/query expansion, etc. (3) Resolving the efficiency challenge in processing keyword search on structured data by utilizing and efficiently maintaining materialized views. These works deliver significant technical contributions towards building a full-fledged search engine for structured data.
ContributorsLiu, Ziyang (Author) / Chen, Yi (Thesis advisor) / Candan, Kasim S (Committee member) / Davulcu, Hasan (Committee member) / Jagadish, H V (Committee member) / Arizona State University (Publisher)
Created2011
Description
Reliable extraction of human pose features that are invariant to view angle and body shape changes is critical for advancing human movement analysis. In this dissertation, the multifactor analysis techniques, including the multilinear analysis and the multifactor Gaussian process methods, have been exploited to extract such invariant pose features from video data by decomposing various key contributing factors, such as pose, view angle, and body shape, in the generation of the image observations. Experimental results have shown that the resulting pose features extracted using the proposed methods exhibit excellent invariance properties to changes in view angles and body shapes. Furthermore, using the proposed invariant multifactor pose features, a suite of simple while effective algorithms have been developed to solve the movement recognition and pose estimation problems. Using these proposed algorithms, excellent human movement analysis results have been obtained, and most of them are superior to those obtained from state-of-the-art algorithms on the same testing datasets. Moreover, a number of key movement analysis challenges, including robust online gesture spotting and multi-camera gesture recognition, have also been addressed in this research. To this end, an online gesture spotting framework has been developed to automatically detect and learn non-gesture movement patterns to improve gesture localization and recognition from continuous data streams using a hidden Markov network. In addition, the optimal data fusion scheme has been investigated for multicamera gesture recognition, and the decision-level camera fusion scheme using the product rule has been found to be optimal for gesture recognition using multiple uncalibrated cameras. Furthermore, the challenge of optimal camera selection in multi-camera gesture recognition has also been tackled. A measure to quantify the complementary strength across cameras has been proposed. Experimental results obtained from a real-life gesture recognition dataset have shown that the optimal camera combinations identified according to the proposed complementary measure always lead to the best gesture recognition results.
ContributorsPeng, Bo (Author) / Qian, Gang (Thesis advisor) / Ye, Jieping (Committee member) / Li, Baoxin (Committee member) / Spanias, Andreas (Committee member) / Arizona State University (Publisher)
Created2011
Description
A dual-channel directional digital hearing aid (DHA) front-end using a fully differential difference amplifier (FDDA) based Microphone interface circuit (MIC) for a capacitive Micro Electro Mechanical Systems (MEMS) microphones and an adaptive-power analog font end (AFE) is presented. The Microphone interface circuit based on FDDA converts the capacitance variations into voltage signal, achieves a noise of 32 dB SPL (sound pressure level) and an SNR of 72 dB, additionally it also performs single to differential conversion allowing for fully differential analog signal chain. The analog front-end consists of 40dB VGA and a power scalable continuous time sigma delta ADC, with 68dB SNR dissipating 67u¬W from a 1.2V supply. The ADC implements a self calibrating feedback DAC, for calibrating the 2nd order non-linearity. The VGA and power scalable ADC is fabricated on 0.25 um CMOS TSMC process. The dual channels of the DHA are precisely matched and achieve about 0.5dB gain mismatch, resulting in greater than 5dB directivity index. This will enable a highly integrated and low power DHA
ContributorsNaqvi, Syed Roomi (Author) / Kiaei, Sayfe (Thesis advisor) / Bakkaloglu, Bertan (Committee member) / Chae, Junseok (Committee member) / Barnby, Hugh (Committee member) / Aberle, James T., 1961- (Committee member) / Arizona State University (Publisher)
Created2011
Description
Silicon nanowires were grown epitaxially on Si (100) and (111) surfaces using the Vapor-Liquid-Solid (VLS) mechanism under both thermal and plasma enhanced growth conditions. Nanowire morphology was investigated as a function of temperature, time, disilane partial pressure and substrate preparation. Silicon nanowires synthesized in low temperature plasma typically curved compared to the linear nanowires grown under simple thermal conditions. The nanowires tended bend more with increasing disilane partial gas pressure up to 25 x10-3 mTorr. The nanowire curvature measured geometrically is correlated with the shift of the main silicon peak obtained in Raman spectroscopy. A mechanistic hypothesis was proposed to explain the bending during plasma activated growth. Additional driving forces related to electrostatic and Van der Waals forces were also discussed. Deduced from a systematic variation of a three-step experimental protocol, the mechanism for bending was associated with asymmetric deposition rate along the outer and inner wall of nanowire. The conditions leading to nanowire branching were also examined using a two-step growth process. Branching morphologies were examined as a function of plasma powers between 1.5 W and 3.5 W. Post-annealing thermal and plasma-assisted treatments in hydrogen were compared to understand the influences in the absence of an external silicon source (otherwise supplied by disilane). Longer and thicker nanowires were associated with longer annealing times due to an Ostwald-like ripening effect. The roles of surface diffusion, gas diffusion, etching and deposition rates were examined.
ContributorsJoun, Hee-Joung (Author) / Petuskey, William T. (Thesis advisor) / Drucker, Jeff (Committee member) / Chizmeshya, Andrew (Committee member) / Arizona State University (Publisher)
Created2011
Description
Demand for biosensor research applications is growing steadily. According to a new report by Frost & Sullivan, the biosensor market is expected to reach $14.42 billion by 2016. Clinical diagnostic applications continue to be the largest market for biosensors, and this demand is likely to continue through 2016 and beyond. Biosensor technology for use in clinical diagnostics, however, requires translational research that moves bench science and theoretical knowledge toward marketable products. Despite the high volume of academic research to date, only a handful of biomedical devices have become viable commercial applications. Academic research must increase its focus on practical uses for biosensors. This dissertation is an example of this increased focus, and discusses work to advance microfluidic-based protein biosensor technologies for practical use in clinical diagnostics. Four areas of work are discussed: The first involved work to develop reusable/reconfigurable biosensors that are useful in applications like biochemical science and analytical chemistry that require detailed sensor calibration. This work resulted in a prototype sensor and an in-situ electrochemical surface regeneration technique that can be used to produce microfluidic-based reusable biosensors. The second area of work looked at non-specific adsorption (NSA) of biomolecules, which is a persistent challenge in conventional microfluidic biosensors. The results of this work produced design methods that reduce the NSA. The third area of work involved a novel microfluidic sensing platform that was designed to detect target biomarkers using competitive protein adsorption. This technique uses physical adsorption of proteins to a surface rather than complex and time-consuming immobilization procedures. This method enabled us to selectively detect a thyroid cancer biomarker, thyroglobulin, in a controlled-proteins cocktail and a cardiovascular biomarker, fibrinogen, in undiluted human serum. The fourth area of work involved expanding the technique to produce a unique protein identification method; Pattern-recognition. A sample mixture of proteins generates a distinctive composite pattern upon interaction with a sensing platform consisting of multiple surfaces whereby each surface consists of a distinct type of protein pre-adsorbed on the surface. The utility of the "pattern-recognition" sensing mechanism was then verified via recognition of a particular biomarker, C-reactive protein, in the cocktail sample mixture.
ContributorsChoi, Seokheun (Author) / Chae, Junseok (Thesis advisor) / Tao, Nongjian (Committee member) / Yu, Hongyu (Committee member) / Forzani, Erica (Committee member) / Arizona State University (Publisher)
Created2011
Description
A relatively simple subset of nanotechnology - nanofluids - can be obtained by adding nanoparticles to conventional base fluids. The promise of these fluids stems from the fact that relatively low particle loadings (typically <1% volume fractions) can significantly change the properties of the base fluid. This research explores how low volume fraction nanofluids, composed of common base-fluids, interact with light energy. Comparative experimentation and modeling reveals that absorbing light volumetrically (i.e. in the depth of the fluid) is fundamentally different from surface-based absorption. Depending on the particle material, size, shape, and volume fraction, a fluid can be changed from being mostly transparent to sunlight (in the case of water, alcohols, oils, and glycols) to being a very efficient volumetric absorber of sunlight. This research also visualizes, under high levels of irradiation, how nanofluids undergo interesting, localized phase change phenomena. For this, images were taken of bubble formation and boiling in aqueous nanofluids heated by a hot wire and by a laser. Infrared thermography was also used to quantify this phenomenon. Overall, though, this research reveals the possibility for novel solar collectors in which the working fluid directly absorbs light energy and undergoes phase change in a single step. Modeling results indicate that these improvements can increase a solar thermal receiver's efficiency by up to 10%.
ContributorsTaylor, Robert (Author) / Phelan, Patrick E (Thesis advisor) / Adrian, Ronald (Committee member) / Trimble, Steve (Committee member) / Posner, Jonathan (Committee member) / Maracas, George (Committee member) / Arizona State University (Publisher)
Created2011
Description
Amorphous oxide semiconductors are promising new materials for various optoelectronic applications. In this study, improved electrical and optical properties upon thermal and microwave processing of mixed-oxide semiconductors are reported. First, arsenic-doped silicon was used as a model system to understand susceptor-assisted microwave annealing. Mixed oxide semiconductor films of indium zinc oxide (IZO) and indium gallium zinc oxide (IGZO) were deposited by room-temperature RF sputtering on flexible polymer substrates. Thermal annealing in different environments - air, vacuum and oxygen was done. Electrical and optical characterization was carried out before and after annealing. The degree of reversal in the degradation in electrical properties of the thin films upon annealing in oxygen was assessed by subjecting samples to subsequent vacuum anneals. To further increase the conductivity of the IGZO films, Ag layers of various thicknesses were embedded between two IGZO layers. Optical performance of the multilayer structures was improved by susceptor-assisted microwave annealing and furnace-annealing in oxygen environment without compromising on their electrical conductivity. The post-processing of the films in different environments was used to develop an understanding of mechanisms of carrier generation, transport and optical absorption. This study establishes IGZO as a viable transparent conductor, which can be deposited at room-temperature and processed by thermal and microwave annealing to improve electrical and optical performance for applications in flexible electronics and optoelectronics.
ContributorsGadre, Mandar (Author) / Alford, Terry L. (Thesis advisor) / Schroder, Dieter (Committee member) / Krause, Stephen (Committee member) / Theodore, David (Committee member) / Arizona State University (Publisher)
Created2011
Description
Alzheimer's Disease (AD) is a debilitating neurodegenerative disease. The disease leads to dementia and loss of cognitive functions and affects about 4.5 million people in the United States. It is the 7th leading cause of death and is a huge financial burden on the healthcare industry. There are no means of diagnosing the disease before neurodegeneration is significant and sadly there is no cure that controls its progression. The protein beta-amyloid or Aâ plays an important role in the progression of the disease. It is formed from the cleavage of the Amyloid Precursor Protein by two enzymes - â and ã-secretases and is found in the plaques that are deposits found in Alzheimer brains. This work describes the generation of therapeutics based on inhibition of the cleavage by â-secretase. Using in-vitro recombinant antibody display libraries to screen for single chain variable fragment (scFv) antibodies; this work describes the isolation and characterization of scFv that target the â-secretase cleavage site on APP. This approach is especially relevant since non-specific inhibition of the enzyme may have undesirable effects since the enzyme has been shown to have other important substrates. The scFv iBSEC1 successfully recognized APP, reduced â-secretase cleavage of APP and reduced Aâ levels in a cell model of Alzheimer's Disease. This work then describes the first application of bispecific antibody therapeutics to Alzheimer's Disease. iBSEC1 scFv was combined with a proteolytic scFv that enhances the "good" pathway (á-secretase cleavage) that results in alternative cleavage of APP to generate the bispecific tandem scFv - DIA10D. DIA10D reduced APP cleavage by â-secretase and steered it towards the "good" pathway thus increasing the generation of the fragment sAPPá which is neuroprotective. Finally, treatment with iBSEC1 is evaluated for reduced oxidative stress, which is observed in cells over expressing APP when they are exposed to stress. Recombinant antibody based therapeutics like scFv have several advantages since they retain the high specificity of the antibodies but are safer since they lack the constant region and are smaller, potentially facilitating easier delivery to the brain
ContributorsBoddapati, Shanta (Author) / Sierks, Michael (Thesis advisor) / Arizona State University (Publisher)
Created2011